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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Fuzzy Quantifier-Based Fuzzy Rough Sets


DOI: http://dx.doi.org/10.15439/2022F231

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 269278 ()

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Abstract. In this paper we apply vague quantification to fuzzy rough sets to introduce fuzzy quantifier based fuzzy rough sets (FQFRS), an intuitive generalizationof fuzzy rough sets. We show how several existing models fit in this generalization as well as how it inspires novel models that may improve these existing models. In addition, we introduce several new binary quantification models. Finally, we introduce an adaptation of FQFRS that allows seamless integration of outlier detection algorithms to enhance the robustness of the applications based on FQFRS.


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